Object Boundary Guided Semantic Segmentation

Semantic segmentation has been a major topic in computer vision, and has played an important role in understanding object classes as well as object localizations. Recent development in deep learning, especially in fully-convolutional neural network, has enabled pixel-level labeling for more accurate results. However most of the previous works, including FCN, did not take object boundary into consideration. In fact, since the originally labeled ground truth does not provide with a clean object boundary, the labeled contours and background objects have been both ignored as background class. In this work, we propose an elegant object boundary guided FCN (OBG-FCN) network, which uses the prior knowledge of object boundary from training to achieve better class accuracy and segmentation details. To this end, we first relabel the object contours, and use the FCN network to specially learn to find the whereabouts of the object and contours. Then we transform the output of this branch to 21 classes and use it as a mask to refine the detail shapes of the objects. An end-to-end learning is then applied to finetune the transforming parameters which reconsider the combination of object-background-boundary in the final segmentation decision. We apply the proposed method in PASCAL VOC segmentation benchmark, and have achieved 87.4% mean IU (15% relative improvements compared to FCN and around 10% improvement compared to CRFRNN), and our edge model has shown to be stable and accurate even at accuracy level of FCN-2s.